Chen Shen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Chen Shen has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Jiayao Li
College of Chemical & Pharmaceutical Engineering, Hebei University of Science & Technology, Shijiazhuang 050018, China
1 shared publication - Jijie Zhang
School of Materials Science and Engineering Nankai University Tianjin People's Republic of China
1 shared publication - Ning Li
School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
1 shared publication - Wenchao Peng
Tianjin University
1 shared publication - Xiaomeng Guo
College of Chemical & Pharmaceutical Engineering, Hebei University of Science & Technology, Shijiazhuang 050018, China
1 shared publication - Zijian Zhang
College of Chemical & Pharmaceutical Engineering, Hebei University of Science & Technology, Shijiazhuang 050018, China
1 shared publication
Latest Publications
- Open Access
- 22 October 2026
- Published by MDPI AG in Microorganisms
- Vol. 14 (11) , 2414
Recent Advances in Microbial and Enzymatic Bioremediation of Recalcitrant Contaminants: Integrating Metabolic Pathways with Frontier Technologies and Machine Learning
Environmental pollution is a pressing global issue, characterized by diverse contaminants and the unsustainability of traditional treatment methods. Biodegradation offers an effective strategy to address this challenge, targeting pollutants such as antibiotics, pharmaceuticals, pesticides, dyes, polycyclic aromatic hydrocarbons, petroleum-derived compounds, plastics, persistent polymers, gaseous substances and so on. This review systematically summarizes recent advances in the use of microorganisms and enzymes for pollutant removal or transformation. Studies on microorganisms and enzymes capable of degrading different pollutant classes are presented, along with their degradation mechanisms and practical feasibility, providing guidance for researchers and industry practitioners and highlighting candidates with higher degradation potential. Furthermore, emerging approaches, including enzyme evolution and gene editing, synergistic strategies, immobilization technologies, and machine-learning-assisted optimization, are discussed as ways to enhance biodegradation stability and efficiency. Notably, directed evolution has achieved up to 400-fold enhancement in catalytic efficiency, while machine-learning models have reached high classification accuracy (AUC = 0.99) in predicting pollutant degradation.
- 22 October 2026
- Published by MDPI AG in Microorganisms
- Vol. 14 (11) , 2414
Open AccessRecent Advances in Microbial and Enzymatic Bioremediation of Recalcitrant Contaminants: Integrating Metabolic Pathways with Frontier Technologies and Machine Learning
Environmental pollution is a pressing global issue, characterized by diverse contaminants and the unsustainability of traditional treatment methods. Biodegradation offers an effective strategy to address this challenge, targeting pollutants such as antibiotics, pharmaceuticals, pesticides, dyes, polycyclic aromatic hydrocarbons, petroleum-derived compounds, plastics, persistent polymers, gaseous substances and so on. This review systematically summarizes recent advances in the use of microorganisms and enzymes for pollutant removal or transformation. Studies on microorganisms and enzymes capable of degrading different pollutant classes are presented, along with their degradation mechanisms and practical feasibility, providing guidance for researchers and industry practitioners and highlighting candidates with higher degradation potential. Furthermore, emerging approaches, including enzyme evolution and gene editing, synergistic strategies, immobilization technologies, and machine-learning-assisted optimization, are discussed as ways to enhance biodegradation stability and efficiency. Notably, directed evolution has achieved up to 400-fold enhancement in catalytic efficiency, while machine-learning models have reached high classification accuracy (AUC = 0.99) in predicting pollutant degradation.